It is important that an optical flame detector is able to detect the presence of various types of flame in as reliable a manner as possible. This requires that the flame detector can discriminate between flames and other sources of infrared radiation. Commonly, optical flame detection is carried out in the infrared portion of the spectrum narrowed about 4.3 μm, a hydrocarbon emission peak.
An optical flame detector generally functions by analyzing portions of the spectrum emitted by a flame and/or analyzing the temporal flicker of that flame. Typically, the spectral signature of a flame in the Mid-Wave InfraRed (MWIR) spectrum (˜3 μm to 5 μm) contains several key features that are not easily replicated by false alarm sources. The UV-C waveband from approximately 180 nm to 290 nm also contains unique spectral information that when combined with the MWIR helps to discriminate a flame based on its spectral signature.
In the prior art, simple flame detectors employ a single sensor, and a warning is provided whenever the signal sensed by the detector exceeds a particular threshold level. This simple approach suffers from false triggering, because it is unable to discriminate between flames and other bright objects, such as incandescent light bulbs, hot industrial processes such as welding, warm hands waved in front of the detector, a lit cigarette, and even sunlight.
Attempts have been made to overcome this problem by sensing radiation at two or more wavelengths. A comparison of the relative strengths of the signals sensed at each wavelength permits greater discrimination over false sources than when sensing at only a single wavelength. The prior art of optical flame detection utilizes several bandpass filters strategically chosen to provide a good measure of discrimination. A bandpass filter is generally accepted to have a well-defined lower, 50% of peak transmission, “cuton” wavelength and upper, 50% of peak transmission, “cutoff” wavelength. Furthermore, the bandpass filter will have a full-width, half maximum (FWHM) bandwidth from between 1% and 13% of the center wavelength. The bandpass, center wavelength, and number of filters are valuable tools for the system engineer; appropriate choices can offer good rejection of false alarms. For example, U.S. Pat. No. 5,995,008 to King, et al, discloses an optical fire detector apparatus employing at least two sensors configured with bandpass filters responsive to overlapping spectral bands. One bandpass filter is centered at 4.5 μm with a bandwidth of about 0.15 μm to insure sampling of CO2, and a second bandpass filter is configured with a passband of about 0.35 μm and centered such that either the upper or the lower filter response boundary wavelength both filters are roughly coincident. According to King, et al., a third filter may be added to sample other parts of the spectrum.
Indeed, given enough resources, it is theoretically possible to design a system that will provide 100% rejection of all conceivable false alarms. Practically, however, the system engineer is limited in their choice of the number of filters that they can reasonably implement within a particular cost constraint. Additional filters quickly increase the cost of the sensor since each filter, a costly item itself, requires another sensor which is usually more expensive than the filter, as well as the lower cost items such as another preamplifier, another analog-to-digital converter channel, and more processing power. In addition, bandpass filters are generally more expensive the more narrow the chosen bandwidth. The lower bandwidths provide enhanced discrimination of flames, however, this increases the cost of the detector. Finally, the more narrow the bandwidth, the more sensitive the associated sensor must become in order to provide an adequate signal to noise ratio for the lesser number of photons arriving at the sensor. Given the practical limitations imposed by sensor cost on the optical flame detection method, the bandpass filter can produce more problems than it solves.
Some system engineers have sought to alleviate some of the cost of adding more bandpass filters to the system by utilizing pyroelectric detectors such as lithium tantalate (LiTaO3). The pyroelectric detector is a lower cost solution than other MWIR detectors such as lead selenide (PbSe) and certainly less expensive than the more exotic detector materials such as indium arsenide (InAs), indium lead (InSb), mercury cadmium telluride (MCT), or mercury cadmium zinc telluride (MCZT). However, the pyroelectric detector suffers an intrinsic flaw when considering applications that require rugged detectors. Namely, any crystal that exhibits the pyroelectric effect must also, to some degree, exhibit a piezoelectric effect. In other words, pyroelectric detectors are sensitive to sounds and vibration, as well as electromagnetic radiation, and will output a response in proportion to both of these stimuli. This is highly undesirable when considering applications in which the noise and/or vibration environment is expected to be significant such as aircraft, land/sea vehicles, industrial operations, factories, etc.